Modeling workflows: Identifying the most predictive features in healthcare operational processes.
Colm Crowley1,2, Steven Guitron1, Joseph Son3
1Massachusetts General Hospital, Boston, MA, United States of America.
Plos One
|June 12, 2020
Summary
Optimizing healthcare operations requires accurate predictions. Congestion features from hospital information systems provide the most predictive models for operational processes, improving patient care and satisfaction.
Area of Science:
- Healthcare Operations Research
- Health Informatics
- Process Modeling
Background:
- Healthcare systems face challenges with limited resources and increasing patient volumes.
- Accurate prediction of patient flow, workflow surges, and wait times is crucial for quality of care and patient satisfaction.
Purpose of the Study:
- To identify optimal operational features for developing accurate and concise healthcare process models.
- To enhance the effectiveness of interventions by improving process modeling.
Main Methods:
- Evaluated 84 operational features derived from Hospital Information Systems across four diverse workflows.
- Categorized features into congestion, customer, resource, task, and time subgroups.
- Utilized linear regression and random forest models for feature selection.
Main Results:
- Congestion feature sets consistently yielded the most predictive models for operational processes.
- This approach resulted in more concise models with fewer predictors, independent of workflow type or selection model.
- Feature selection identified key indicators for optimizing healthcare workflows.
Conclusions:
- Congestion features are highly effective predictors for healthcare operational processes.
- The findings support the use of specific operational features to build more accurate and efficient healthcare process models.
- This research provides a foundation for data-driven interventions to improve healthcare system performance.
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